Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations8954786
Missing cells94
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 GiB
Average record size in memory160.0 B

Variable types

DateTime4
Numeric11
Text4
Categorical1

Alerts

bill_total_billed is highly overall correlated with bill_total_net and 3 other fieldsHigh correlation
bill_total_net is highly overall correlated with bill_total_billed and 3 other fieldsHigh correlation
bill_total_tax is highly overall correlated with bill_total_billed and 3 other fieldsHigh correlation
payment_amount is highly overall correlated with bill_total_billed and 4 other fieldsHigh correlation
payment_total_tip is highly overall correlated with payment_amountHigh correlation
sales_revenue_with_tax is highly overall correlated with bill_total_billed and 3 other fieldsHigh correlation
order_take_out_type_label is highly imbalanced (61.9%) Imbalance
bill_total_billed is highly skewed (γ1 = 144.4166783) Skewed
bill_total_discount_item_level is highly skewed (γ1 = 149.7226489) Skewed
bill_total_gratuity is highly skewed (γ1 = 479.1136713) Skewed
bill_total_net is highly skewed (γ1 = 134.8548939) Skewed
bill_total_tax is highly skewed (γ1 = 261.6218339) Skewed
bill_total_voided is highly skewed (γ1 = 2929.1235) Skewed
order_duration_seconds is highly skewed (γ1 = 238.195982) Skewed
payment_amount is highly skewed (γ1 = 2048.525811) Skewed
payment_total_tip is highly skewed (γ1 = 2960.142341) Skewed
sales_revenue_with_tax is highly skewed (γ1 = 144.4157221) Skewed
bill_uuid has unique values Unique
bill_total_billed has 229152 (2.6%) zeros Zeros
bill_total_discount_item_level has 8267805 (92.3%) zeros Zeros
bill_total_gratuity has 8748366 (97.7%) zeros Zeros
bill_total_net has 229618 (2.6%) zeros Zeros
bill_total_tax has 1326428 (14.8%) zeros Zeros
bill_total_voided has 8715185 (97.3%) zeros Zeros
payment_amount has 230480 (2.6%) zeros Zeros
payment_count has 213726 (2.4%) zeros Zeros
payment_total_tip has 5270130 (58.9%) zeros Zeros
sales_revenue_with_tax has 229169 (2.6%) zeros Zeros

Reproduction

Analysis started2025-02-12 00:06:56.533921
Analysis finished2025-02-12 00:22:30.077393
Duration15 minutes and 33.54 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct5832313
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
Minimum2024-07-01 00:00:01
Maximum2025-01-01 21:41:43
Invalid dates0
Invalid dates (%)0.0%
2025-02-11T19:22:30.313551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:22:30.424550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

bill_total_billed
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct52348
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.748312
Minimum-5731.5
Maximum74928.61
Zeros229152
Zeros (%)2.6%
Negative13818
Negative (%)0.2%
Memory size68.3 MiB
2025-02-11T19:22:30.539550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5731.5
5-th percentile3
Q110.44
median21.89
Q344.65
95-th percentile111
Maximum74928.61
Range80660.11
Interquartile range (IQR)34.21

Descriptive statistics

Standard deviation83.3765
Coefficient of variation (CV)2.2688525
Kurtosis84263.107
Mean36.748312
Median Absolute Deviation (MAD)14.27
Skewness144.41668
Sum3.2907327 × 108
Variance6951.6407
MonotonicityNot monotonic
2025-02-11T19:22:30.653550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 229152
 
2.6%
14.75 36083
 
0.4%
8 33994
 
0.4%
7 32542
 
0.4%
6 31031
 
0.3%
11.3 31014
 
0.3%
15.5 30194
 
0.3%
9.04 28185
 
0.3%
10.17 27950
 
0.3%
3 27381
 
0.3%
Other values (52338) 8447260
94.3%
ValueCountFrequency (%)
-5731.5 2
< 0.1%
-4712.1 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2433.2 1
< 0.1%
-1200 1
< 0.1%
-1158.49 1
< 0.1%
-1000 1
< 0.1%
-822.01 1
< 0.1%
-773.39 1
< 0.1%
ValueCountFrequency (%)
74928.61 1
< 0.1%
33928.25 1
< 0.1%
25131.48 1
< 0.1%
23132.39 1
< 0.1%
22606.78 1
< 0.1%
22088.31 1
< 0.1%
21000 1
< 0.1%
20551.26 1
< 0.1%
19866 1
< 0.1%
19021.08 1
< 0.1%

bill_total_discount_item_level
Real number (ℝ)

Skewed  Zeros 

Distinct21111
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84038025
Minimum-91.64
Maximum5490.92
Zeros8267805
Zeros (%)92.3%
Negative557
Negative (%)< 0.1%
Memory size68.3 MiB
2025-02-11T19:22:30.748550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-91.64
5-th percentile0
Q10
median0
Q30
95-th percentile2.99
Maximum5490.92
Range5582.56
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.404221
Coefficient of variation (CV)11.190436
Kurtosis52064.222
Mean0.84038025
Median Absolute Deviation (MAD)0
Skewness149.72265
Sum7525425.3
Variance88.439374
MonotonicityNot monotonic
2025-02-11T19:22:30.837599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8267805
92.3%
2 25179
 
0.3%
1 23432
 
0.3%
3 18672
 
0.2%
5 17377
 
0.2%
4 16656
 
0.2%
6 13106
 
0.1%
10 11484
 
0.1%
8 9767
 
0.1%
1.5 9302
 
0.1%
Other values (21101) 542006
 
6.1%
ValueCountFrequency (%)
-91.64 1
< 0.1%
-74.3 1
< 0.1%
-66.25 1
< 0.1%
-62.84 1
< 0.1%
-59.06 2
< 0.1%
-58.62 2
< 0.1%
-55 1
< 0.1%
-53.6 1
< 0.1%
-53.26 1
< 0.1%
-50.27 1
< 0.1%
ValueCountFrequency (%)
5490.92 1
< 0.1%
4763.45 1
< 0.1%
4227.21 1
< 0.1%
4182 1
< 0.1%
4152 1
< 0.1%
3624.71 1
< 0.1%
3242.04 1
< 0.1%
3115.35 1
< 0.1%
3050 1
< 0.1%
2973.65 1
< 0.1%

bill_total_gratuity
Real number (ℝ)

Skewed  Zeros 

Distinct8527
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24089563
Minimum-60
Maximum11935.53
Zeros8748366
Zeros (%)97.7%
Negative41
Negative (%)< 0.1%
Memory size68.3 MiB
2025-02-11T19:22:30.928549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11935.53
Range11995.53
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.409234
Coefficient of variation (CV)34.908205
Kurtosis517986.54
Mean0.24089563
Median Absolute Deviation (MAD)0
Skewness479.11367
Sum2157168.8
Variance70.715217
MonotonicityNot monotonic
2025-02-11T19:22:31.032553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8748366
97.7%
0.32 5527
 
0.1%
0.15 4679
 
0.1%
0.64 2297
 
< 0.1%
0.24 1816
 
< 0.1%
3.6 1591
 
< 0.1%
0.45 1336
 
< 0.1%
0.12 1300
 
< 0.1%
0.3 1298
 
< 0.1%
0.48 1166
 
< 0.1%
Other values (8517) 185410
 
2.1%
ValueCountFrequency (%)
-60 1
< 0.1%
-37.8 1
< 0.1%
-19.08 1
< 0.1%
-18.4 1
< 0.1%
-12 1
< 0.1%
-11.79 1
< 0.1%
-11.76 1
< 0.1%
-9.58 1
< 0.1%
-3.87 1
< 0.1%
-3.2 1
< 0.1%
ValueCountFrequency (%)
11935.53 1
< 0.1%
6005 1
< 0.1%
4029.21 1
< 0.1%
4003.25 1
< 0.1%
4001.2 1
< 0.1%
3402.7 1
< 0.1%
3245 1
< 0.1%
2916.32 1
< 0.1%
2504.6 1
< 0.1%
2492.6 1
< 0.1%

bill_total_net
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct39096
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.898938
Minimum-5072.12
Maximum66308.5
Zeros229618
Zeros (%)2.6%
Negative13499
Negative (%)0.2%
Memory size68.3 MiB
2025-02-11T19:22:31.120107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5072.12
5-th percentile2.8
Q19.63
median20
Q341
95-th percentile102
Maximum66308.5
Range71380.62
Interquartile range (IQR)31.37

Descriptive statistics

Standard deviation77.543558
Coefficient of variation (CV)2.2874923
Kurtosis71061.104
Mean33.898938
Median Absolute Deviation (MAD)13
Skewness134.85489
Sum3.0355773 × 108
Variance6013.0034
MonotonicityNot monotonic
2025-02-11T19:22:31.215108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 229618
 
2.6%
8 87408
 
1.0%
18 74780
 
0.8%
10 72169
 
0.8%
16 69570
 
0.8%
15 68653
 
0.8%
5 67514
 
0.8%
12 64947
 
0.7%
6 63182
 
0.7%
7 61035
 
0.7%
Other values (39086) 8095910
90.4%
ValueCountFrequency (%)
-5072.12 2
< 0.1%
-4170 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2342.09 1
< 0.1%
-1200 1
< 0.1%
-1158.49 1
< 0.1%
-1000 1
< 0.1%
-755 1
< 0.1%
-695 1
< 0.1%
ValueCountFrequency (%)
66308.5 1
< 0.1%
30025 1
< 0.1%
22384.5 1
< 0.1%
22240.25 1
< 0.1%
20253.15 1
< 0.1%
20050 1
< 0.1%
20006 1
< 0.1%
19866 1
< 0.1%
18985 1
< 0.1%
17256 1
< 0.1%

bill_total_tax
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct11021
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8493749
Minimum-659.38
Maximum8620.11
Zeros1326428
Zeros (%)14.8%
Negative10194
Negative (%)0.1%
Memory size68.3 MiB
2025-02-11T19:22:31.306125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-659.38
5-th percentile0
Q10.46
median1.44
Q33.45
95-th percentile9.85
Maximum8620.11
Range9279.49
Interquartile range (IQR)2.99

Descriptive statistics

Standard deviation7.1533854
Coefficient of variation (CV)2.5105104
Kurtosis254554.13
Mean2.8493749
Median Absolute Deviation (MAD)1.24
Skewness261.62183
Sum25515542
Variance51.170922
MonotonicityNot monotonic
2025-02-11T19:22:31.396224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1326428
 
14.8%
1.3 53326
 
0.6%
1.04 52435
 
0.6%
0.65 48969
 
0.5%
0.9 48135
 
0.5%
0.2 44161
 
0.5%
2.08 44076
 
0.5%
0.52 43785
 
0.5%
1.17 43043
 
0.5%
0.91 42729
 
0.5%
Other values (11011) 7207699
80.5%
ValueCountFrequency (%)
-659.38 2
< 0.1%
-542.1 1
< 0.1%
-91.11 1
< 0.1%
-88.97 1
< 0.1%
-73.85 1
< 0.1%
-73.42 1
< 0.1%
-72.8 1
< 0.1%
-68.9 1
< 0.1%
-67.01 1
< 0.1%
-60.09 1
< 0.1%
ValueCountFrequency (%)
8620.11 1
< 0.1%
3903.25 1
< 0.1%
2891.23 1
< 0.1%
2600.78 1
< 0.1%
2038.31 1
< 0.1%
1765.08 1
< 0.1%
1566.26 1
< 0.1%
1505.53 1
< 0.1%
1334.81 1
< 0.1%
1300 1
< 0.1%

bill_total_voided
Real number (ℝ)

Skewed  Zeros 

Distinct10798
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93750928
Minimum-4501.5
Maximum1000000
Zeros8715185
Zeros (%)97.3%
Negative624
Negative (%)< 0.1%
Memory size68.3 MiB
2025-02-11T19:22:31.489112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4501.5
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1000000
Range1004501.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation336.59661
Coefficient of variation (CV)359.03283
Kurtosis8699773.9
Mean0.93750928
Median Absolute Deviation (MAD)0
Skewness2929.1235
Sum8395195
Variance113297.28
MonotonicityNot monotonic
2025-02-11T19:22:31.588721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8715185
97.3%
8 5293
 
0.1%
10 4570
 
0.1%
9 4543
 
0.1%
16 4454
 
< 0.1%
12 4278
 
< 0.1%
6 4047
 
< 0.1%
5 3942
 
< 0.1%
15 3707
 
< 0.1%
7 3702
 
< 0.1%
Other values (10788) 201065
 
2.2%
ValueCountFrequency (%)
-4501.5 1
< 0.1%
-1697 1
< 0.1%
-1665.9 1
< 0.1%
-1215.15 1
< 0.1%
-1173.15 1
< 0.1%
-784.81 1
< 0.1%
-630.49 1
< 0.1%
-538.55 1
< 0.1%
-500 1
< 0.1%
-400 1
< 0.1%
ValueCountFrequency (%)
1000000 1
< 0.1%
43178.4 1
< 0.1%
37405.34 1
< 0.1%
26407.46 1
< 0.1%
23172.36 1
< 0.1%
21370.88 1
< 0.1%
21274.49 1
< 0.1%
20835 1
< 0.1%
20805.02 1
< 0.1%
20071.6 1
< 0.1%

bill_uuid
Text

Unique 

Distinct8954786
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
2025-02-11T19:22:43.693002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length49
Mean length48.990803
Min length36

Characters and Unicode

Total characters438702159
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8954786 ?
Unique (%)100.0%

Sample

1st row240701091701~4D63608F-523C-4EFF-9A4F-78D6C44B5159
2nd row240701133659~3A0BEDE2-E9E5-484D-B909-780E485F0D69
3rd row240701111931~670EEB85-E939-4924-B92F-C95076B7E930
4th row240701150558~3D6F473E-D5EF-4BB8-AFAB-30DB2A0764C0
5th row240701124059~0521794C-51FC-4C30-A368-995CE4DE105F
ValueCountFrequency (%)
240701111931~670eeb85-e939-4924-b92f-c95076b7e930 1
 
< 0.1%
241231185507~62c6632e-2957-4f0d-8aff-cf89ca378fb4 1
 
< 0.1%
241226123511~35605033-f24b-46ed-96cd-5ee5ad0af892 1
 
< 0.1%
241226123530~8fe0f8fe-8881-4c1a-9e02-b62ee88c17d5 1
 
< 0.1%
241226135739~19c42fec-33be-4a4c-b1ab-add25a7721c0 1
 
< 0.1%
241226155336~589994ce-d6cf-46d3-9470-b4fcee070e8f 1
 
< 0.1%
241226214300~babd37be-35a6-4e9e-850a-467bd2689dca 1
 
< 0.1%
241227163610~521ce078-d840-46e8-a8a3-8a8e983952bb 1
 
< 0.1%
241227174057~0a0b75d8-71b8-4625-80d8-91924ba9e18e 1
 
< 0.1%
241227175419~fdca9a8b-8515-47a8-b438-e95fe1b7177b 1
 
< 0.1%
Other values (8954776) 8954776
> 99.9%
2025-02-11T19:22:49.751706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 40995320
 
9.3%
1 38564892
 
8.8%
2 38477651
 
8.8%
- 35819144
 
8.2%
0 33441331
 
7.6%
9 24267620
 
5.5%
8 24253871
 
5.5%
3 23951068
 
5.5%
5 22977703
 
5.2%
7 21778270
 
5.0%
Other values (14) 134175289
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438702159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 40995320
 
9.3%
1 38564892
 
8.8%
2 38477651
 
8.8%
- 35819144
 
8.2%
0 33441331
 
7.6%
9 24267620
 
5.5%
8 24253871
 
5.5%
3 23951068
 
5.5%
5 22977703
 
5.2%
7 21778270
 
5.0%
Other values (14) 134175289
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438702159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 40995320
 
9.3%
1 38564892
 
8.8%
2 38477651
 
8.8%
- 35819144
 
8.2%
0 33441331
 
7.6%
9 24267620
 
5.5%
8 24253871
 
5.5%
3 23951068
 
5.5%
5 22977703
 
5.2%
7 21778270
 
5.0%
Other values (14) 134175289
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438702159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 40995320
 
9.3%
1 38564892
 
8.8%
2 38477651
 
8.8%
- 35819144
 
8.2%
0 33441331
 
7.6%
9 24267620
 
5.5%
8 24253871
 
5.5%
3 23951068
 
5.5%
5 22977703
 
5.2%
7 21778270
 
5.0%
Other values (14) 134175289
30.6%
Distinct184
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
Minimum2024-07-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-11T19:22:49.845254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:22:49.974611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_duration_seconds
Real number (ℝ)

Skewed 

Distinct118347
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8790.9683
Minimum-44
Maximum2.8941241 × 108
Zeros122
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size68.3 MiB
2025-02-11T19:22:50.075833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-44
5-th percentile33
Q1110
median1159
Q33377
95-th percentile9741
Maximum2.8941241 × 108
Range2.8941246 × 108
Interquartile range (IQR)3267

Descriptive statistics

Standard deviation398439.78
Coefficient of variation (CV)45.323766
Kurtosis89740.817
Mean8790.9683
Median Absolute Deviation (MAD)1106
Skewness238.19598
Sum7.872124 × 1010
Variance1.5875426 × 1011
MonotonicityNot monotonic
2025-02-11T19:22:50.187692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 32247
 
0.4%
43 31866
 
0.4%
40 31857
 
0.4%
45 31685
 
0.4%
46 31684
 
0.4%
44 31677
 
0.4%
41 31666
 
0.4%
39 31576
 
0.4%
38 31420
 
0.4%
47 31330
 
0.3%
Other values (118337) 8637778
96.5%
ValueCountFrequency (%)
-44 1
 
< 0.1%
-19 1
 
< 0.1%
0 122
 
< 0.1%
1 2160
 
< 0.1%
2 5564
0.1%
3 4459
< 0.1%
4 6277
0.1%
5 6740
0.1%
6 6043
0.1%
7 5715
0.1%
ValueCountFrequency (%)
289412413 1
< 0.1%
196826278 1
< 0.1%
190832960 1
< 0.1%
179540675 1
< 0.1%
163422375 1
< 0.1%
162689496 1
< 0.1%
154425999 1
< 0.1%
151728800 1
< 0.1%
150226620 1
< 0.1%
142630398 1
< 0.1%
Distinct5441306
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
Minimum2015-10-10 19:13:37
Maximum2025-01-01 18:57:30
Invalid dates0
Invalid dates (%)0.0%
2025-02-11T19:22:50.298649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:22:50.420902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct5803496
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
Minimum2024-07-01 00:00:01
Maximum2025-01-01 21:41:43
Invalid dates0
Invalid dates (%)0.0%
2025-02-11T19:22:50.527976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:22:50.657841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_take_out_type_label
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
dinein
7443139 
takeout
975989 
onlineorder
 
270192
bartab
 
247617
delivery
 
17849

Length

Max length11
Median length6
Mean length6.2638418
Min length6

Characters and Unicode

Total characters56091363
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdinein
2nd rowdinein
3rd rowdinein
4th rowdinein
5th rowdinein

Common Values

ValueCountFrequency (%)
dinein 7443139
83.1%
takeout 975989
 
10.9%
onlineorder 270192
 
3.0%
bartab 247617
 
2.8%
delivery 17849
 
0.2%

Length

2025-02-11T19:22:50.767076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-11T19:22:50.866839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dinein 7443139
83.1%
takeout 975989
 
10.9%
onlineorder 270192
 
3.0%
bartab 247617
 
2.8%
delivery 17849
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 15426662
27.5%
i 15174319
27.1%
e 8995210
16.0%
d 7731180
13.8%
t 2199595
 
3.9%
o 1516373
 
2.7%
a 1471223
 
2.6%
k 975989
 
1.7%
u 975989
 
1.7%
r 805850
 
1.4%
Other values (4) 818973
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56091363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 15426662
27.5%
i 15174319
27.1%
e 8995210
16.0%
d 7731180
13.8%
t 2199595
 
3.9%
o 1516373
 
2.7%
a 1471223
 
2.6%
k 975989
 
1.7%
u 975989
 
1.7%
r 805850
 
1.4%
Other values (4) 818973
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56091363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 15426662
27.5%
i 15174319
27.1%
e 8995210
16.0%
d 7731180
13.8%
t 2199595
 
3.9%
o 1516373
 
2.7%
a 1471223
 
2.6%
k 975989
 
1.7%
u 975989
 
1.7%
r 805850
 
1.4%
Other values (4) 818973
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56091363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 15426662
27.5%
i 15174319
27.1%
e 8995210
16.0%
d 7731180
13.8%
t 2199595
 
3.9%
o 1516373
 
2.7%
a 1471223
 
2.6%
k 975989
 
1.7%
u 975989
 
1.7%
r 805850
 
1.4%
Other values (4) 818973
 
1.5%
Distinct8221819
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
2025-02-11T19:23:03.356807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length49
Mean length48.990803
Min length36

Characters and Unicode

Total characters438702159
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7804409 ?
Unique (%)87.2%

Sample

1st row240701091615~EF1C6E91-B6C4-4DF1-8A92-1B024197FEC3
2nd row240701133212~A4C33BFA-A54F-4627-B0C2-7428427FB5DE
3rd row240701111717~6074B0AB-2432-484D-BC3F-55CC5D731818
4th row240701150511~EAA68F41-20ED-4FC7-B902-B0B3878AEC82
5th row240701123917~27764E1C-7E7F-4293-ABC7-5921310A654A
ValueCountFrequency (%)
241019211330~7bbb205b-b2c5-4a41-be48-3adbbb373eb1 92
 
< 0.1%
240914174325~01482558-0947-4341-8662-cf4eb18cc045 52
 
< 0.1%
240913225853~632e2003-bd52-4c2d-a42d-cbe90c35535a 43
 
< 0.1%
241018211013~83c159f1-9998-42ae-b8e2-06154a4a495f 39
 
< 0.1%
240726211619~ce2ffa2c-3bb0-49f9-a695-f3bb0d074c73 38
 
< 0.1%
241231213143~f495c51e-a537-4a1b-8e16-da97a132d190 36
 
< 0.1%
241012223955~38741a41-132a-4818-a003-c9dc425e4ed0 33
 
< 0.1%
241012194904~25c7b69f-c134-421a-ae23-927c922fa034 32
 
< 0.1%
241108234708~11accc10-5fbc-4559-84f0-73e7a8340afa 31
 
< 0.1%
240920223808~092fe466-3c2a-46ef-8898-684dce87cd8b 30
 
< 0.1%
Other values (8221809) 8954360
> 99.9%
2025-02-11T19:23:08.339807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 40907741
 
9.3%
1 39112820
 
8.9%
2 37861492
 
8.6%
- 35819144
 
8.2%
0 33250178
 
7.6%
8 24439114
 
5.6%
9 24274206
 
5.5%
3 23813475
 
5.4%
5 22916348
 
5.2%
7 22009395
 
5.0%
Other values (14) 134298246
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438702159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 40907741
 
9.3%
1 39112820
 
8.9%
2 37861492
 
8.6%
- 35819144
 
8.2%
0 33250178
 
7.6%
8 24439114
 
5.6%
9 24274206
 
5.5%
3 23813475
 
5.4%
5 22916348
 
5.2%
7 22009395
 
5.0%
Other values (14) 134298246
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438702159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 40907741
 
9.3%
1 39112820
 
8.9%
2 37861492
 
8.6%
- 35819144
 
8.2%
0 33250178
 
7.6%
8 24439114
 
5.6%
9 24274206
 
5.5%
3 23813475
 
5.4%
5 22916348
 
5.2%
7 22009395
 
5.0%
Other values (14) 134298246
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438702159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 40907741
 
9.3%
1 39112820
 
8.9%
2 37861492
 
8.6%
- 35819144
 
8.2%
0 33250178
 
7.6%
8 24439114
 
5.6%
9 24274206
 
5.5%
3 23813475
 
5.4%
5 22916348
 
5.2%
7 22009395
 
5.0%
Other values (14) 134298246
30.6%

payment_amount
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct60807
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.638782
Minimum-5731.5
Maximum522752.27
Zeros230480
Zeros (%)2.6%
Negative13469
Negative (%)0.2%
Memory size68.3 MiB
2025-02-11T19:23:08.419695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5731.5
5-th percentile3
Q110.97
median23.47
Q348.87
95-th percentile125.05
Maximum522752.27
Range528483.77
Interquartile range (IQR)37.9

Descriptive statistics

Standard deviation198.72923
Coefficient of variation (CV)4.8901375
Kurtosis5349711.7
Mean40.638782
Median Absolute Deviation (MAD)15.56
Skewness2048.5258
Sum3.6391159 × 108
Variance39493.307
MonotonicityNot monotonic
2025-02-11T19:23:08.512799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 230480
 
2.6%
14.75 33121
 
0.4%
7 28667
 
0.3%
6 27963
 
0.3%
8 27770
 
0.3%
15.5 26348
 
0.3%
3 25961
 
0.3%
5 23937
 
0.3%
11.3 23790
 
0.3%
12 23258
 
0.3%
Other values (60797) 8483491
94.7%
ValueCountFrequency (%)
-5731.5 2
< 0.1%
-4712.1 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2433.2 1
< 0.1%
-1200 1
< 0.1%
-1158.49 1
< 0.1%
-1000 1
< 0.1%
-822.01 1
< 0.1%
-773.39 1
< 0.1%
ValueCountFrequency (%)
522752.27 1
< 0.1%
86864.14 1
< 0.1%
39933.25 1
< 0.1%
29134.73 1
< 0.1%
27161.6 1
< 0.1%
26607.98 1
< 0.1%
25333.31 1
< 0.1%
25050.63 1
< 0.1%
24348.26 1
< 0.1%
23786.14 1
< 0.1%

payment_count
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98444251
Minimum0
Maximum41
Zeros213726
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size68.3 MiB
2025-02-11T19:23:08.587758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1926432
Coefficient of variation (CV)0.19568761
Kurtosis536.27749
Mean0.98444251
Median Absolute Deviation (MAD)0
Skewness4.3116816
Sum8815472
Variance0.037111402
MonotonicityNot monotonic
2025-02-11T19:23:08.653576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 8678322
96.9%
0 213726
 
2.4%
2 55558
 
0.6%
3 4949
 
0.1%
4 1226
 
< 0.1%
5 538
 
< 0.1%
6 210
 
< 0.1%
7 111
 
< 0.1%
8 52
 
< 0.1%
9 29
 
< 0.1%
Other values (15) 65
 
< 0.1%
ValueCountFrequency (%)
0 213726
 
2.4%
1 8678322
96.9%
2 55558
 
0.6%
3 4949
 
0.1%
4 1226
 
< 0.1%
5 538
 
< 0.1%
6 210
 
< 0.1%
7 111
 
< 0.1%
8 52
 
< 0.1%
9 29
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
22 2
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 2
< 0.1%
17 2
< 0.1%
16 3
< 0.1%

payment_total_tip
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct12894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6542116
Minimum-253.14
Maximum522715.64
Zeros5270130
Zeros (%)58.9%
Negative104
Negative (%)< 0.1%
Memory size68.3 MiB
2025-02-11T19:23:08.735653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-253.14
5-th percentile0
Q10
median0
Q34.07
95-th percentile16.81
Maximum522715.64
Range522968.78
Interquartile range (IQR)4.07

Descriptive statistics

Standard deviation175.31856
Coefficient of variation (CV)47.977125
Kurtosis8824410.7
Mean3.6542116
Median Absolute Deviation (MAD)0
Skewness2960.1423
Sum32722683
Variance30736.599
MonotonicityNot monotonic
2025-02-11T19:23:08.827212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5270130
58.9%
5 176041
 
2.0%
2 148820
 
1.7%
10 139597
 
1.6%
1 128279
 
1.4%
3 101440
 
1.1%
4 70049
 
0.8%
20 58749
 
0.7%
6 57866
 
0.6%
8 49234
 
0.5%
Other values (12884) 2754581
30.8%
ValueCountFrequency (%)
-253.14 1
< 0.1%
-207.36 1
< 0.1%
-198.42 1
< 0.1%
-189.5 1
< 0.1%
-179.9 1
< 0.1%
-129.8 1
< 0.1%
-101.7 1
< 0.1%
-99 1
< 0.1%
-83.88 1
< 0.1%
-79.7 1
< 0.1%
ValueCountFrequency (%)
522715.64 1
< 0.1%
23762.38 1
< 0.1%
17668.02 1
< 0.1%
11097.36 1
< 0.1%
5180.16 1
< 0.1%
4350.09 1
< 0.1%
4050.63 1
< 0.1%
3797 1
< 0.1%
3080 1
< 0.1%
2940 1
< 0.1%

sales_revenue_with_tax
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct52346
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.749018
Minimum-5731.5
Maximum74928.61
Zeros229169
Zeros (%)2.6%
Negative13817
Negative (%)0.2%
Memory size68.3 MiB
2025-02-11T19:23:08.943521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5731.5
5-th percentile3
Q110.44
median21.89
Q344.65
95-th percentile111
Maximum74928.61
Range80660.11
Interquartile range (IQR)34.21

Descriptive statistics

Standard deviation83.37668
Coefficient of variation (CV)2.2688138
Kurtosis84262.373
Mean36.749018
Median Absolute Deviation (MAD)14.27
Skewness144.41572
Sum3.2907959 × 108
Variance6951.6707
MonotonicityNot monotonic
2025-02-11T19:23:09.043366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 229169
 
2.6%
14.75 36083
 
0.4%
8 33994
 
0.4%
7 32542
 
0.4%
6 31031
 
0.3%
11.3 31014
 
0.3%
15.5 30194
 
0.3%
9.04 28185
 
0.3%
10.17 27950
 
0.3%
3 27381
 
0.3%
Other values (52336) 8447243
94.3%
ValueCountFrequency (%)
-5731.5 2
< 0.1%
-4712.1 1
< 0.1%
-3323.28 1
< 0.1%
-2625 1
< 0.1%
-2433.2 1
< 0.1%
-1200 1
< 0.1%
-1158.49 1
< 0.1%
-1000 1
< 0.1%
-822.01 1
< 0.1%
-773.39 1
< 0.1%
ValueCountFrequency (%)
74928.61 1
< 0.1%
33928.25 1
< 0.1%
25131.48 1
< 0.1%
23132.39 1
< 0.1%
22606.78 1
< 0.1%
22088.31 1
< 0.1%
21000 1
< 0.1%
20551.26 1
< 0.1%
19866 1
< 0.1%
19021.08 1
< 0.1%
Distinct601
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.3 MiB
2025-02-11T19:23:09.316355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters573106304
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row39e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df
2nd row39e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df
3rd row39e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df
4th row39e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df
5th row39e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df
ValueCountFrequency (%)
5e8a21ead84851c212c2cae58849de4d37bc0babfcab05ceff51350412eb3e94 86044
 
1.0%
6c4ddbba39d1529689be33c4769223b1194ba2dcb9e20f266d18face3279d00a 80194
 
0.9%
a7de7d703442f619ab305b7752fd215d85b28bad1dba9f01715d38fec9ce0efb 78213
 
0.9%
1fba23fbdd499646399a75417304d971f5fdea72bce5ba4b0d9e6086ac093892 75070
 
0.8%
68e559beb80a2ae3cbdbf07043ebf35fbbccd85494216a2f32b31da76ece5ac0 74152
 
0.8%
5dc459c71f4739c3dbd5872b4d442295864cf5715f704b19e02e1a581453561a 72908
 
0.8%
92128c7fc22f3f685e9914450a0bd855e258aafff9351663260b080a4a7c5188 68561
 
0.8%
6afc5202235f3b742e6482c94536ab8462f6022054eccb0c6d5630c211c0a7fe 66480
 
0.7%
5ece4507d7aec26fdcca66082fcc77457f79cea602c1a82122c7ea496d37c493 65448
 
0.7%
3c5a0e6460177d012e070306ba429335066823de1e25aa1ffbd3df7966cacd81 64779
 
0.7%
Other values (591) 8222937
91.8%
2025-02-11T19:23:09.535695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 38593238
 
6.7%
c 37575268
 
6.6%
e 37459095
 
6.5%
0 36401005
 
6.4%
f 36188145
 
6.3%
a 35852502
 
6.3%
d 35836090
 
6.3%
3 35746675
 
6.2%
b 35664639
 
6.2%
6 35388616
 
6.2%
Other values (6) 208401031
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 573106304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 38593238
 
6.7%
c 37575268
 
6.6%
e 37459095
 
6.5%
0 36401005
 
6.4%
f 36188145
 
6.3%
a 35852502
 
6.3%
d 35836090
 
6.3%
3 35746675
 
6.2%
b 35664639
 
6.2%
6 35388616
 
6.2%
Other values (6) 208401031
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 573106304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 38593238
 
6.7%
c 37575268
 
6.6%
e 37459095
 
6.5%
0 36401005
 
6.4%
f 36188145
 
6.3%
a 35852502
 
6.3%
d 35836090
 
6.3%
3 35746675
 
6.2%
b 35664639
 
6.2%
6 35388616
 
6.2%
Other values (6) 208401031
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 573106304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 38593238
 
6.7%
c 37575268
 
6.6%
e 37459095
 
6.5%
0 36401005
 
6.4%
f 36188145
 
6.3%
a 35852502
 
6.3%
d 35836090
 
6.3%
3 35746675
 
6.2%
b 35664639
 
6.2%
6 35388616
 
6.2%
Other values (6) 208401031
36.4%
Distinct9162
Distinct (%)0.1%
Missing94
Missing (%)< 0.1%
Memory size68.3 MiB
2025-02-11T19:23:09.675627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length49
Mean length47.36324
Min length20

Characters and Unicode

Total characters424123230
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique341 ?
Unique (%)< 0.1%

Sample

1st row161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
2nd row161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
3rd row161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
4th row161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
5th row161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
ValueCountFrequency (%)
190405092446~8646fe39-600c-4318-bea7-0a31d4082914 431432
 
4.8%
220405145647~93ca8d12-f48a-44ec-bf83-2674a4dc6094 73563
 
0.8%
200122200826~9afddbd1-59a4-4016-ad66-830100e21632 43295
 
0.5%
220808130537~4ff8785d-4a7b-49ff-a7fe-b8551b6591d0 38721
 
0.4%
200217111829~15f6aab8-87ad-4e90-a5ca-f6ed08fc1e4f 37727
 
0.4%
150101000000~99c97c5b-82ae-48b5-a578-cc528ee0c57c 37421
 
0.4%
cc66dfde-4181-46f5-a04d-d14424650700 37380
 
0.4%
201209123916~356b9122-4031-4a51-886f-8dcea9d8afc5 36170
 
0.4%
230711173726~12a0de3a-7ef0-4477-bbee-4b422343fab9 33057
 
0.4%
220528101742~8c27e6e6-4320-45ad-aea9-fdb28a8364cd 31541
 
0.4%
Other values (9152) 8154385
91.1%
2025-02-11T19:23:09.852934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 36033458
 
8.5%
- 35818720
 
8.4%
0 35781314
 
8.4%
1 35628843
 
8.4%
2 33658061
 
7.9%
3 24410284
 
5.8%
9 23365754
 
5.5%
5 23032400
 
5.4%
8 23003734
 
5.4%
6 21311769
 
5.0%
Other values (23) 132078893
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 424123230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 36033458
 
8.5%
- 35818720
 
8.4%
0 35781314
 
8.4%
1 35628843
 
8.4%
2 33658061
 
7.9%
3 24410284
 
5.8%
9 23365754
 
5.5%
5 23032400
 
5.4%
8 23003734
 
5.4%
6 21311769
 
5.0%
Other values (23) 132078893
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 424123230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 36033458
 
8.5%
- 35818720
 
8.4%
0 35781314
 
8.4%
1 35628843
 
8.4%
2 33658061
 
7.9%
3 24410284
 
5.8%
9 23365754
 
5.5%
5 23032400
 
5.4%
8 23003734
 
5.4%
6 21311769
 
5.0%
Other values (23) 132078893
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 424123230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 36033458
 
8.5%
- 35818720
 
8.4%
0 35781314
 
8.4%
1 35628843
 
8.4%
2 33658061
 
7.9%
3 24410284
 
5.8%
9 23365754
 
5.5%
5 23032400
 
5.4%
8 23003734
 
5.4%
6 21311769
 
5.0%
Other values (23) 132078893
31.1%

Interactions

2025-02-11T19:20:54.622621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:26.827888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:42.504976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:58.293477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:12.819847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:27.170780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:41.640003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:56.170598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:10.891104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:25.069054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:39.814953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:55.907284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:28.286182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:43.857312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:59.585570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:14.152915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:28.468060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:42.959515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:57.523499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:12.149033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:26.472103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:41.112252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:57.178465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:29.667326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:45.392846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:00.864916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:15.434875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:29.789916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:44.282223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:58.844980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:13.422107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:27.836202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:42.401233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:58.405840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:31.068795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:46.784456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:02.214576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:16.652949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:31.090846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:45.590111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:00.222290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:14.672253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:29.178197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:43.702072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:59.635184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:32.493441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:48.268000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:03.516712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:17.914712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:32.326195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:46.888248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:01.535669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:15.949183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:30.509317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:45.048622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:21:00.894124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:33.925353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:49.733522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:04.778212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:19.214296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:33.685083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:48.140821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:02.863410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:17.207390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:31.832778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:46.319528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:21:02.246994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:35.396618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:51.261675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:06.143265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:20.585547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:35.040479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:49.524897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:04.139724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:18.579484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:33.242903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:47.678064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:21:03.469606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:36.826080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:52.793172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:07.474381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:21.897823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:36.348272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:50.863318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:05.457737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:19.836737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:34.622072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:49.164890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:21:04.733136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:38.230147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:54.463302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:08.826729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:23.207042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:37.745964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:52.214703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:06.820327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:21.192141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:35.865833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:50.626339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:21:05.974058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:39.615745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:55.748752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:10.158793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:24.546154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:39.054458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:53.518250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:08.126186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:22.495223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:37.192607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:51.910881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:21:07.174794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:41.042880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:18:57.026237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:11.520329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:25.887215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:40.352330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:19:54.831713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:09.514585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:23.760688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:38.509985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-11T19:20:53.234465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-11T19:23:09.966434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bill_total_billedbill_total_discount_item_levelbill_total_gratuitybill_total_netbill_total_taxbill_total_voidedorder_duration_secondsorder_take_out_type_labelpayment_amountpayment_countpayment_total_tipsales_revenue_with_tax
bill_total_billed1.000-0.0760.0770.9990.779-0.0580.4780.0040.9950.2590.4571.000
bill_total_discount_item_level-0.0761.000-0.003-0.076-0.061-0.0010.1070.006-0.073-0.174-0.017-0.076
bill_total_gratuity0.077-0.0031.0000.0770.0780.0060.0820.0030.0900.0260.0230.077
bill_total_net0.999-0.0760.0771.0000.756-0.0580.4730.0040.9940.2590.4540.999
bill_total_tax0.779-0.0610.0780.7561.000-0.0520.4450.0050.7810.2250.4190.779
bill_total_voided-0.058-0.0010.006-0.058-0.0521.0000.1030.000-0.057-0.372-0.017-0.058
order_duration_seconds0.4780.1070.0820.4730.4450.1031.0000.0020.492-0.0480.3510.478
order_take_out_type_label0.0040.0060.0030.0040.0050.0000.0021.0000.0010.0010.0000.004
payment_amount0.995-0.0730.0900.9940.781-0.0570.4920.0011.0000.2590.5210.995
payment_count0.259-0.1740.0260.2590.225-0.372-0.0480.0010.2591.0000.1180.259
payment_total_tip0.457-0.0170.0230.4540.419-0.0170.3510.0000.5210.1181.0000.457
sales_revenue_with_tax1.000-0.0760.0770.9990.779-0.0580.4780.0040.9950.2590.4571.000

Missing values

2025-02-11T19:21:10.133605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-11T19:21:23.563857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

bill_paid_at_localbill_total_billedbill_total_discount_item_levelbill_total_gratuitybill_total_netbill_total_taxbill_total_voidedbill_uuidbusiness_dateorder_duration_secondsorder_seated_at_localorder_closed_at_localorder_take_out_type_labelorder_uuidpayment_amountpayment_countpayment_total_tipsales_revenue_with_taxvenue_xref_idwaiter_uuid
02024-07-01 09:17:0121.810.00.019.302.510.0240701091701~4D63608F-523C-4EFF-9A4F-78D6C44B51592024-07-01462024-07-01 09:16:152024-07-01 09:17:01dinein240701091615~EF1C6E91-B6C4-4DF1-8A92-1B024197FEC321.8110.021.8139e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
12024-07-01 13:37:0048.950.00.043.325.630.0240701133659~3A0BEDE2-E9E5-484D-B909-780E485F0D692024-07-012882024-07-01 13:32:122024-07-01 13:37:00dinein240701133212~A4C33BFA-A54F-4627-B0C2-7428427FB5DE48.9510.048.9539e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
22024-07-01 11:19:3226.940.00.023.843.100.0240701111931~670EEB85-E939-4924-B92F-C95076B7E9302024-07-011352024-07-01 11:17:172024-07-01 11:19:32dinein240701111717~6074B0AB-2432-484D-BC3F-55CC5D73181826.9410.026.9439e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
32024-07-01 15:05:593.960.00.03.770.190.0240701150558~3D6F473E-D5EF-4BB8-AFAB-30DB2A0764C02024-07-01482024-07-01 15:05:112024-07-01 15:05:59dinein240701150511~EAA68F41-20ED-4FC7-B902-B0B3878AEC823.9610.03.9639e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
42024-07-01 12:41:003.360.00.03.200.160.0240701124059~0521794C-51FC-4C30-A368-995CE4DE105F2024-07-011032024-07-01 12:39:172024-07-01 12:41:00dinein240701123917~27764E1C-7E7F-4293-ABC7-5921310A654A3.3610.03.3639e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
52024-07-01 10:01:5516.360.00.014.481.880.0240701100155~9B70DD97-2C09-4EF1-9C7B-8076D84E062B2024-07-01842024-07-01 10:00:312024-07-01 10:01:55dinein240701100031~8AE5F668-E818-4830-AE73-509C16EA774116.3610.016.3639e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
62024-07-01 12:23:4617.420.00.015.422.000.0240701122346~091CE8CA-7135-43F7-8D67-A5261BD30CDA2024-07-011422024-07-01 12:21:242024-07-01 12:23:46dinein240701122124~13DBD4D5-2C64-48C8-80D5-9E9FC3D116DF17.4210.017.4239e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
72024-07-01 11:59:0725.110.00.022.222.890.0240701115907~074476EF-C6EA-49A0-B76E-E99264E1D1A62024-07-011312024-07-01 11:56:562024-07-01 11:59:07dinein240701115656~D9D66137-AFBB-488F-91A3-3EDA3207A8F225.1110.025.1139e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
82024-07-01 15:37:404.970.00.04.400.570.0240701153740~40A8D7C4-425A-44DB-98FF-E663A12E19F62024-07-01192024-07-01 15:37:212024-07-01 15:37:40dinein240701153721~72BC36E3-4E0A-47B7-AE1D-892071933B1C4.9710.04.9739e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
92024-07-01 16:20:1019.000.00.019.000.000.0240701162010~01A577B5-25CB-4908-AF54-73B5428FD83B2024-07-017162024-07-01 16:08:142024-07-01 16:20:10dinein240701160814~B6D53396-87A5-4AE9-A83B-B1A059AE4A8325.0016.019.0039e5b4830d4d9c14db7368a95b65d5463ea3d09520373723430c03a5a453b5df161027134744~719D0E09-5CA7-442A-B9FA-BBBF4083B4FD
bill_paid_at_localbill_total_billedbill_total_discount_item_levelbill_total_gratuitybill_total_netbill_total_taxbill_total_voidedbill_uuidbusiness_dateorder_duration_secondsorder_seated_at_localorder_closed_at_localorder_take_out_type_labelorder_uuidpayment_amountpayment_countpayment_total_tipsales_revenue_with_taxvenue_xref_idwaiter_uuid
89547762024-12-27 17:40:5824.980.000.024.980.00.0241227174057~0A0B75D8-71B8-4625-80D8-91924BA9E18E2024-12-275072024-12-27 17:32:312024-12-27 17:40:58takeout241227173231~6B94AD74-4C11-4977-8A1D-2CD15C39AB3624.9810.024.9845f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817221116163611~F844A24E-44E8-40F2-B6D0-073FF805DBF7
89547772024-12-27 17:54:1978.430.000.078.430.00.0241227175419~FDCA9A8B-8515-47A8-B438-E95FE1B7177B2024-12-2738532024-12-27 16:50:062024-12-27 17:54:19dinein241227165006~8559E840-33DD-47EA-844E-211E68ED17A378.4310.078.4345f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230202164435~D6FB6D1F-F2E2-4DB2-8278-59AAFF09A7F4
89547782024-12-27 21:32:43108.900.000.0108.900.00.0241227213243~3465F212-5737-4B4A-9FE4-B528E630C08D2024-12-2738592024-12-27 20:28:242024-12-27 21:32:43dinein241227202824~BB60AF10-4446-4659-9F0C-B7E3942B62EA108.9010.0108.9045f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230202164435~D6FB6D1F-F2E2-4DB2-8278-59AAFF09A7F4
89547792024-12-28 19:58:0075.420.000.075.420.00.0241228195800~416C102D-5E2B-47B7-9EA3-4D4D56367D962024-12-2830872024-12-28 19:06:332024-12-28 19:58:00dinein241228190633~A3D5144B-B4E1-4855-8718-B7995717B52675.4210.075.4245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6
89547802024-12-28 20:38:2999.920.000.099.920.00.0241228203829~D10C3768-50E1-4E52-8F0A-F5CBA575A5142024-12-2874192024-12-28 18:34:502024-12-28 20:38:29dinein241228183450~DAE45042-47AA-4DF0-A580-4EB72ACB876399.9210.099.9245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6
89547812024-12-29 20:32:5482.420.000.082.420.00.0241229203254~464AA06D-F986-4C51-8A53-A7B8362072D62024-12-2943822024-12-29 19:19:522024-12-29 20:32:54dinein241229191952~96F91DE2-5ECC-4AC9-9228-B0741F3494B182.4210.082.4245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817220831171122~1455DBA8-211C-4281-BF27-8698365CA3EB
89547822024-12-30 20:36:1987.920.000.087.920.00.0241230203619~37348A86-98D1-4113-8EDD-06A3CF61A85B2024-12-3052442024-12-30 19:08:552024-12-30 20:36:19dinein241230190855~BB4604F2-B7BA-487A-99AF-7AEB77CA3EEC87.9210.087.9245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6
89547832024-12-31 14:24:2351.960.000.051.960.00.0241231142423~9D2E9295-9E59-4E76-8242-D230C9FD24942024-12-319632024-12-31 14:08:202024-12-31 14:24:23takeout241231140820~4D531668-3D68-4789-A1DB-BE324D4583D551.9610.051.9645f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817161216233231~64B016BA-D9BA-49C7-8726-4BCB6516A11D
89547842024-12-31 17:00:3449.3511.590.049.350.00.0241231170034~A9B4401D-26DC-4FC5-A4F9-0261D950DB3B2024-12-31592024-12-31 16:59:352024-12-31 17:00:34takeout241231165935~27989697-086C-4F7C-8AB2-8C0A8B2CF3A749.3510.049.3545f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6
89547852024-12-31 18:55:0788.420.000.088.420.00.0241231185507~62C6632E-2957-4F0D-8AFF-CF89CA378FB42024-12-3165562024-12-31 17:05:512024-12-31 18:55:07dinein241231170551~4DEE5611-B193-40FB-858B-0650CE2F725988.4210.088.4245f1736264898588301e2983fd2de6969a9af33aa0f6f8ec588d258c1971c817230522185246~9369CFD3-1EED-48B1-A116-F8A4F6B66FC6